Towards new-generation human-centric smart manufacturing in Industry 5.0: A systematic review

Zhang, Chao; Wang, Zenghui; Zhou, Guanghui; Chang, Fengtian; Ma, Dongxu; Jing, Yanzhen; Cheng, Wei; Ding, Kai; Zhao, Dan

doi:10.1016/j.aei.2023.102121

Cited by 48 publications

(7 citation statements)

References 168 publications

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“…Similarly, the degree to which factor i is influenced, denoted as " i s " is calculated as per Eq. (11).…”

Section: Methodsmentioning

confidence: 99%

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Evaluating the Interrelationships of Industrial 5.0 Development Factors Using an Integration Approach of Fermatean Fuzzy Logic

Lo,

Chan,

Lin

et al. 2024

J. Oper. Intell.

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The maturation of the Industry 4.0 concept has brought numerous benefits to human society. However, it is not without its challenges, including neglect of worker welfare, vulnerability of global supply chains, and environmental degradation. To enhance the adaptability of the Industry 4.0 concept, Industry 5.0 has been developed. As of now, the practical implementation of Industry 5.0 has not yet been fully realized. This paper presents a novel conceptual framwork to analyze and evaluate the complex interrelationships of development factors in Industrial 5.0. Through extensive literature review and prolonged interviews with experts, three critical dimensions and their 18 key factors for the development of Industry 5.0 have been identified. Herein, a combination of Fermatean Fuzzy sets (FFs) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) has been employed to discern the interrelationships among these factors, and an Influential Network Relationship Map (INRM) has been constructed to aid decision-makers in formulating improvement strategies. The results indicate that “Sustainable Development” is the most influential dimension, with factors “Renewable Energy,” “Data-Driven Analysis Technologies,” and “Distributed Control” emerging as the most significant factors within their respective dimensions.

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“…Similarly, the degree to which factor i is influenced, denoted as " i s " is calculated as per Eq. (11).…”

Section: Methodsmentioning

confidence: 99%

“…However, this tech-centric approach overlooked crucial factors such as worker welfare and environmental sustainability. As a response, Industry 5.0 emerged with the goal of integrating technological advancements with human-centric values and sustainable practices [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Interrelationships of Industrial 5.0 Development Factors Using an Integration Approach of Fermatean Fuzzy Logic

Lo,

Chan,

Lin

et al. 2024

J. Oper. Intell.

View full text Add to dashboard Cite

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“…It places the wellbeing of the worker at the centre of the production process, enabling the use advanced technologies that provide prosperity beyond jobs. It complements the existing "Industry 4.0" approach by specifically putting research and innovation at the service of the transition to a sustainable, human-centric smart manufacturing [24], and resilient European industry [25].…”

Section: Related Researchmentioning

confidence: 99%

Identification of Criteria for Enabling the Adoption of Sustainable Maintenance Practice: An Umbrella Review

Vasić,

Orošnjak,

Brkljač

et al. 2024

Sustainability

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The evolution from traditional industrial maintenance to sustainable maintenance (SM) is pivotal within an existing industrial ecosystem. This study, utilising an umbrella review (UR), critically examines this transition, highlighting its increased importance in maintenance decision-making (MDM). Using a sample (n = 20) of reviews, we synthesised meta-, methodological-, and content-based evidence and performed bibliometric, thematic and statistical analyses. For the bibliometric and thematic/conceptual analyses, we used the R bibliometrix package. The results show that the early research focuses mainly on theoretical aspects, while recent studies examine the practical implications. Also, comprehensive studies evaluating the benefits of implementing environmental and social aspects within MDM are still lacking. For that reason, we switched the attention to content-based data, from which we identified 43 distinct criteria discussed. For the analysis of criteria, the Bayesian Network Analysis with Gaussian Copula Graphical Model (BNA-GCGM) method was used. Although the evidence shows that environmental pollution, energy consumption and health and safety of workers are the most discussed criteria, the BNA-GCGM suggests that labour costs, resource consumption, employee satisfaction and energy consumption, among others, are the most influential criteria in the network analysis. Interestingly, after distinguishing studies into pre- and post-2021 research, the results show that pre-2021 research is primarily focused on economic and technical factors, reflecting a profit-oriented approach. The post-2021 analysis suggests a discernible shift towards more balanced considerations by incorporating social and environmental factors, suggesting a more socially responsible approach. Finally, while SM is gaining momentum, further empirical and practical research are required to demonstrate the advantages that SM offers in the light of the upcoming Industry 5.0.

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“…The arrival of Industry 5.0 and its human-centric focus creates immense potential for social media analytics while also relying on such techniques to enable optimized human-machine collaboration (Zhang et al ., 2023). By applying advanced natural language processing and machine learning to massive amounts of unstructured social data, behavioral insights can be extracted to deeply understand user preferences, expectations, and satisfaction (Aldunate et al ., 2022; Shahidzadeh et al ., 2023).…”

Section: Literature Pertaining To the Enterprise Decision-making Systemmentioning

confidence: 99%

Unveiling just-in-time decision support system using social media analytics: a case study on reverse logistics resource recycling

Shahidzadeh,

Shokouhyar

2024

IMDS

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PurposeIn recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).Design/methodology/approachLeveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.FindingsTo substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.Originality/valueThis scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

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Towards new-generation human-centric smart manufacturing in Industry 5.0: A systematic review

Cited by 48 publications

References 168 publications

Evaluating the Interrelationships of Industrial 5.0 Development Factors Using an Integration Approach of Fermatean Fuzzy Logic

Evaluating the Interrelationships of Industrial 5.0 Development Factors Using an Integration Approach of Fermatean Fuzzy Logic

Identification of Criteria for Enabling the Adoption of Sustainable Maintenance Practice: An Umbrella Review

Unveiling just-in-time decision support system using social media analytics: a case study on reverse logistics resource recycling

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